Publication list

2019

  • Hu, Jialu and Gao, Yiqun and Li, Jing and Shang, Xuequn, "Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease", Frontiers in Genetics, 2019, 10, 937.
  • [Bibtex]

    @article{nwpu_index74,
    year = {2019},
    author = {Hu, Jialu and Gao, Yiqun and Li, Jing and Shang, Xuequn},
    journal = {Frontiers in Genetics},
    title = {Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease},
    volume = {10},
    pages = {937},
    abstract = {Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism that can explain the development of disease. However, the discovery of the relationship between lncRNA and disease in biological experiment is costly and time-consuming. Although many computational algorithms have been proposed in the last decade, there still exists much room to improve because of diverse computational limitations. In this paper, we proposed a deep-learning framework, NNLDA, to predict potential lncRNA-disease associations. We compared it with other two widely-used algorithms on a network with 205,959 interactions between 19,166 lncRNAs and 529 diseases. Results show that NNLDA outperforms other existing algorithm in the prediction of lncRNA-disease association. Additionally, NNLDA can be easily applied to large-scale datasets using the technique of mini-batch stochastic gradient descent. To our best knowledge, NNLDA is the first algorithm that uses deep neural networks to predict lncRNA-disease association. The source code of NNLDA can be freely accessed at https://github.com/gao793583308/NNLDA. },
    source = {https://www.frontiersin.org/article/10.3389/fgene.2019.00937},
    }

2018

  • Guo, Yang, Liu, Shuhui, Li, Zhanhuai and Shang, Xuequn, "BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data", BMC bioinformatics, 2018, 19(5), 118.
  • [Bibtex]

    @article{nwpu_index61,
    year = {2018},
    author = {Guo, Yang, Liu, Shuhui, Li, Zhanhuai and Shang, Xuequn},
    journal = {BMC bioinformatics},
    title = {BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data},
    number = {5},
    volume = {19},
    pages = {118},
    }

  • Jialu Hu, Yiqun Gao, Yan Zheng, Xuequn Shang, "KF-finder: Identification of key factors from host-microbial networks in cervical cancer", BMC Systems Biology, 2018, 12(S4), 54.
  • [Bibtex]

    @article{nwpu_index62,
    year = {2018},
    author = {Jialu Hu, Yiqun Gao, Yan Zheng, Xuequn Shang},
    journal = {BMC Systems Biology},
    title = {KF-finder: Identification of key factors from host-microbial networks in cervical cancer},
    number = {S4},
    volume = {12},
    pages = {54},
    }

  • Y Zhang, M Xiang, B Yang, "Hierarchical sparse coding from a Bayesian perspective", Neurocomputing, 2018, 272, 279-293.
  • [Bibtex]

    @article{nwpu_index68,
    year = {2018},
    author = {Y Zhang, M Xiang, B Yang},
    journal = {Neurocomputing},
    title = {Hierarchical sparse coding from a Bayesian perspective},
    volume = {272},
    pages = {279-293},
    }

  • Y Zhang, S Liu, X Shang, M Xiang, "Low-Rank Graph Regularized Sparse Coding", In Pacific Rim International Conference on Artificial Intelligence, 177-190,2018.
  • [Bibtex]

    @conference{nwpu_index70,
    year = {2018},
    author = {Y Zhang, S Liu, X Shang, M Xiang},
    booktitle = {Pacific Rim International Conference on Artificial Intelligence},
    title = {Low-Rank Graph Regularized Sparse Coding},
    pages = {177-190},
    }

  • Jialu Hu, Yiqun Gao, Junhao He, Yan Zheng and Xuequn Shang, "WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks", BMC Bioinformatics, 2018, 19(1), 422.
  • [Bibtex]

    @article{nwpu_index71,
    year = {2018},
    author = {Jialu Hu, Yiqun Gao, Junhao He, Yan Zheng and Xuequn Shang},
    journal = {BMC Bioinformatics},
    title = {WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks},
    number = {1},
    volume = {19},
    pages = {422},
    abstract = {The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users' test datasets},
    source = {https://doi.org/10.1186/s12859-018-2443-4},
    }

  • Hu, Jialu and Zheng, Yan and Shang, Xuequn, "MiteFinderII: a novel tool to identify miniature inverted-repeat transposable elements hidden in eukaryotic genomes", BMC Medical Genomics, 2018, 11(5), 101.
  • [Bibtex]

    @article{nwpu_index72,
    year = {2018},
    author = {Hu, Jialu and Zheng, Yan and Shang, Xuequn},
    journal = {BMC Medical Genomics},
    title = {MiteFinderII: a novel tool to identify miniature inverted-repeat transposable elements hidden in eukaryotic genomes},
    number = {5},
    volume = {11},
    pages = {101},
    abstract = {Miniature inverted-repeat transposable element (MITE) is a type of class II non-autonomous transposable element playing a crucial role in the process of evolution in biology. There is an urgent need to develop bioinformatics tools to effectively identify MITEs on a whole genome-wide scale. However, most of currently existing tools suffer from low ability to deal with large eukaryotic genomes.},
    source = {https://doi.org/10.1186/s12920-018-0418-y},
    }

  • Hu, Jialu and He, Junhao and Gao, Yiqun and Zheng, Yan and Shang, Xuequn, "NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors", In Intelligent Computing Theories and Application, 241-246, Cham,2018, Springer International Publishing.
  • [Bibtex]

    @conference{nwpu_index73,
    year = {2018},
    author = {Hu, Jialu and He, Junhao and Gao, Yiqun and Zheng, Yan and Shang, Xuequn},
    booktitle = {Intelligent Computing Theories and Application},
    title = {NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors},
    pages = {241-246},
    address = {Cham},
    publisher = {Springer International Publishing},
    }

2017

  • Jiajie Peng, Kun Bai, Xuequn Shang, Guohua Wang, Hansheng Xue, Shuilin Jin, Liang Cheng, Yadong Wang, Jin Chen, "Predicting disease-related genes using integrated biomedical networks ", BMC Genomics, 2017, 18(1), 1043.
  • [Bibtex]

    @article{nwpu_index27,
    year = {2017},
    author = {Jiajie Peng, Kun Bai, Xuequn Shang, Guohua Wang, Hansheng Xue, Shuilin Jin, Liang Cheng, Yadong Wang, Jin Chen},
    journal = {BMC Genomics},
    title = {Predicting disease-related genes using integrated biomedical networks },
    number = {1},
    volume = {18},
    pages = {1043},
    }

  • Danelishvili, L and Shulzhenko, N and Jjj, Chinison and Babrak, L and Hu, J. and Morgun, A and Burrows, G and Bermudez, L. E., "Mycobacterium tuberculosis proteome response to anti-tuberculosis compounds reveals metabolic "escape" pathways that prolong bacterial survival", Antimicrobial Agents & Chemotherapy, 2017.
  • [Bibtex]

    @article{nwpu_index55,
    year = {2017},
    author = {Danelishvili, L and Shulzhenko, N and Jjj, Chinison and Babrak, L and Hu, J. and Morgun, A and Burrows, G and Bermudez, L. E.},
    journal = {Antimicrobial Agents & Chemotherapy},
    title = {Mycobacterium tuberculosis proteome response to anti-tuberculosis compounds reveals metabolic "escape" pathways that prolong bacterial survival},
    }

  • Hu, Jialu and Shang, Xuequn, "Detection of Network Motif Based on a Novel Graph Canonization Algorithm from Transcriptional Regulation Networks", Molecules, 2017, 22(12), 2194.
  • [Bibtex]

    @article{nwpu_index56,
    year = {2017},
    author = {Hu, Jialu and Shang, Xuequn},
    journal = {Molecules},
    title = {Detection of Network Motif Based on a Novel Graph Canonization Algorithm from Transcriptional Regulation Networks},
    number = {12},
    volume = {22},
    pages = {2194},
    }

  • Peng, Jiajie and Wang, Honggang and Lu, Junya and Hui, Weiwei and Wang, Yadong and Shang, Xuequn, "Identifying term relations cross different gene ontology categories", BMC Bioinformatics, 2017, 18(16), 573.
  • [Bibtex]

    @article{nwpu_index57,
    year = {2017},
    author = {Peng, Jiajie and Wang, Honggang and Lu, Junya and Hui, Weiwei and Wang, Yadong and Shang, Xuequn},
    journal = {BMC Bioinformatics},
    title = {Identifying term relations cross different gene ontology categories},
    number = {16},
    volume = {18},
    pages = {573},
    abstract = {The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important.},
    source = {https://doi.org/10.1186/s12859-017-1959-3},
    }

  • Jiajie Peng Weiwei Hui and Xuequn Shang, "Measuring phenotype-phenotype similarity through the interactome", In BIBM Workshop on Biological Ontologies and Knowledge Bases,2017.
  • [Bibtex]

    @conference{nwpu_index58,
    year = {2017},
    author = {Jiajie Peng Weiwei Hui and Xuequn Shang},
    booktitle = {BIBM Workshop on Biological Ontologies and Knowledge Bases},
    title = {Measuring phenotype-phenotype similarity through the interactome},
    }

  • Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Junya Lu, Qianqian Li and Xuequn Shang, "Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach", In GIW / BIOINFO 2017,2017.
  • [Bibtex]

    @conference{nwpu_index59,
    year = {2017},
    author = { Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Junya Lu, Qianqian Li and Xuequn Shang},
    booktitle = {GIW / BIOINFO 2017},
    title = {Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach},
    }

  • Jiajie Peng and Junya Lu and Xuequn Shang and Jin Chen, "Identifying consistent disease subnetworks using DNet", Methods, 2017, 131, 104-110.
  • [Bibtex]

    @article{nwpu_index60,
    year = {2017},
    author = {Jiajie Peng and Junya Lu and Xuequn Shang and Jin Chen},
    journal = {Methods},
    title = {Identifying consistent disease subnetworks using DNet},
    volume = {131},
    pages = {104-110},
    source = {http://www.sciencedirect.com/science/article/pii/S1046202317300610},
    }

  • Jialu Hu, Yan Zheng, and Xuequn Shang, "MiteFinder: A fast approach to identify miniature inverted-repeat transposable elements on a genome-wide scale", In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 164-168, Kansas City, NOV 13-16 2017.
  • [Bibtex]

    @conference{nwpu_index63,
    year = {2017},
    author = {Jialu Hu, Yan Zheng, and Xuequn Shang},
    booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
    title = {MiteFinder: A fast approach to identify miniature inverted-repeat transposable elements on a genome-wide scale},
    month = {NOV 13-16},
    pages = {164-168},
    address = {Kansas City},
    }

  • Y Zhang, M Xiang, B Yang, "Low-rank preserving embedding", Pattern Recognition, 2017, 70, 112-125.
  • [Bibtex]

    @article{nwpu_index66,
    year = {2017},
    author = {Y Zhang, M Xiang, B Yang},
    journal = {Pattern Recognition},
    title = {Low-rank preserving embedding},
    volume = {70},
    pages = {112-125},
    }

  • Y Zhang, M Xiang, B Yang, "Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search", Pattern Recognition, 2017, 70, 75-88.
  • [Bibtex]

    @article{nwpu_index67,
    year = {2017},
    author = {Y Zhang, M Xiang, B Yang},
    journal = {Pattern Recognition},
    title = {Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search},
    volume = {70},
    pages = {75-88},
    }

2016

  • Jiajie Peng, Qianqian Li, Bolin Chen, Jialu Hu and Xuequn Shang, "Analyzing factors involved in the HPO-based semantic similarity calculation", In BIBM Workshop on Biological Ontologies and Knowledge Bases, Shenzhen, 12 2016, HIT.
  • [Bibtex]

    @conference{nwpu_index7,
    year = {2016},
    author = {Jiajie Peng, Qianqian Li, Bolin Chen, Jialu Hu and Xuequn Shang},
    booktitle = {BIBM Workshop on Biological Ontologies and Knowledge Bases},
    title = {Analyzing factors involved in the HPO-based semantic similarity calculation},
    month = {12},
    address = {Shenzhen},
    organization = {HIT},
    }

  • Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu, "Identifying individual-cancer-related genes by rebalancing the training samples", IEEE Transactions on Nanobioscience, 2016, 15(4), 309-315.
  • [Bibtex]

    @article{nwpu_index16,
    year = {2016},
    author = {Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu},
    journal = {IEEE Transactions on Nanobioscience},
    title = {Identifying individual-cancer-related genes by rebalancing the training samples},
    number = {4},
    volume = {15},
    pages = {309-315},
    abstract = {The identification of individual-cancer-related genes typically is an imbalanced classification issue. The number of known cancer-related genes is far less than the number of all unknown genes, which makes it very hard to detect novel predictions from such imbalanced training samples. A regular machine learning method can either only detect genes related to all cancers or add clinical knowledge to circumvent this issue. In this study, we introduce a training sample rebalancing strategy to overcome this issue by using a two-step logistic regression and a random resampling method. The two-step logistic regression is to select a set of genes that related to all cancers. While the random resampling method is performed to further classify those genes associated with individual cancers. The issue of imbalanced classification is circumvented by randomly adding positive instances related to other cancers at first, and then excluding those unrelated predictions according to the overall performance at the following step. Numerical experiments show that the proposed resampling method is able to identify cancer-related genes even when the number of known genes related to it is small. The final predictions for all individual cancers achieve AUC values around 0.93 by using the leave-one-out cross validation method, which is very promising, compared with existing methods.},
    source = {http://ieeexplore.ieee.org/document/7451278/authors?ctx=authors},
    }

  • Xuequn Shang, Yu Wang, Bolin Chen, "Identifying essential proteins based on dynamic PPI networks and RNA-Seq datasets", SCIENCE CHINA Information Science, 2016, 59, 070106.
  • [Bibtex]

    @article{nwpu_index17,
    year = {2016},
    author = {Xuequn Shang, Yu Wang, Bolin Chen},
    journal = {SCIENCE CHINA Information Science},
    title = {Identifying essential proteins based on dynamic PPI networks and RNA-Seq datasets},
    volume = {59},
    pages = {070106},
    abstract = {The identification of essential proteins is not only important for understanding organism structure on the molecular level, but also beneficial to drug-target detection and genetic disease prevention. Traditional methods often employ various centrality indices of static protein-protein interaction (PPI) networks and/or gene expression profiles to predict essential proteins. However, the prediction accuracy of most methods still has room to be further improved. In this study, we propose a strategy to increase the prediction accuracy of essential protein identification in three ways. Firstly, RNA-Seq datasets are employed to construct integrated dynamic PPI networks. Using a RNA-Seq dataset is expected to give more accurate predictions than using microarray gene expression profiles. Secondly, a novel integrated dynamic PPI network is constructed by considering both the co-expression pattern and the co-expression level of the RNA-Seq data. Thirdly, a novel two-step strategy is proposed to identify essential proteins from two known centrality indices. Numerical experiments have shown that the proposed strategy can increase the prediction accuracy dramatically, which can be generalized to many existing methods and centrality indices.},
    source = {http://link.springer.com/article/10.1007/s11432-016-5583-z},
    }

  • Jiajie Peng, Hongxiang Li, Yongzhuang Liu, Liran Juan, Qinghua Jiang, Yadong Wang and Jin Chen, "InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology", BMC Genomics, 2016, 17(5), 530.
  • [Bibtex]

    @article{nwpu_index28,
    year = {2016},
    author = {Jiajie Peng, Hongxiang Li, Yongzhuang Liu, Liran Juan, Qinghua Jiang, Yadong Wang and Jin Chen},
    journal = {BMC Genomics},
    title = {InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology},
    number = {5},
    volume = {17},
    pages = {530},
    }

  • Jiajie Peng, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, and Jin Chen, "Measuring Phenotype Semantic Similarity using Human Phenotype Ontology", In Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on,2016, IEEE.
  • [Bibtex]

    @conference{nwpu_index29,
    year = {2016},
    author = {Jiajie Peng, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, and Jin Chen},
    booktitle = {Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on},
    title = {Measuring Phenotype Semantic Similarity using Human Phenotype Ontology},
    organization = {IEEE},
    }

  • Jiajie Peng, Tao Wang, Jianping Hu, Yadong Wang, Jin Chen, "Constructing Networks of Organelle Functional Modules in Arabidopsis ", Current Genomics, 2016, 17(5), 427-438.
  • [Bibtex]

    @article{nwpu_index30,
    year = {2016},
    author = {Jiajie Peng, Tao Wang, Jianping Hu, Yadong Wang, Jin Chen},
    journal = {Current Genomics},
    title = {Constructing Networks of Organelle Functional Modules in Arabidopsis },
    number = {5},
    volume = {17},
    pages = {427-438},
    }

  • Jiajie Peng, Tao Wang, Jixuan Wang, Yadong Wang, Jin Chen, "Extending gene ontology with gene association networks", Bioinformatics, 2016, 32(8), 1185-1194.
  • [Bibtex]

    @article{nwpu_index33,
    year = {2016},
    author = {Jiajie Peng, Tao Wang, Jixuan Wang, Yadong Wang, Jin Chen},
    journal = {Bioinformatics},
    title = {Extending gene ontology with gene association networks},
    number = {8},
    volume = {32},
    pages = {1185-1194},
    }

  • Cieply B., Park JW., Nakauka-Ddamba A., Bebee TW., Guo Y., Shang X., Lengner CJ., Xing Y, "Carstens RP.+(2016) Multiphasic and dynamic changes in alternative splicing during induction of pluripotency are coordinated by numerous RNA binding proteins", Cell Reports, 2016, 15, 1-9.
  • [Bibtex]

    @article{nwpu_index48,
    year = {2016},
    author = {Cieply B., Park JW., Nakauka-Ddamba A., Bebee TW., Guo Y., Shang X., Lengner CJ., Xing Y},
    journal = {Cell Reports},
    title = {Carstens RP.+(2016) Multiphasic and dynamic changes in alternative splicing during induction of pluripotency are coordinated by numerous RNA binding proteins},
    volume = {15},
    pages = {1-9},
    }

  • Yang Y., Park JW., Bebee TW., Warzecha CC., Guo Y., Shang X., Xing Y., Carstens RP, "Determination of a comprehensive alternative splicing regulatory network and the combinatorial regulation by key factors during the epithelial to mesenchymal transition", Molecular and Cellular Biology, 2016, 36(11), 1704-1719.
  • [Bibtex]

    @article{nwpu_index49,
    year = {2016},
    author = {Yang Y., Park JW., Bebee TW., Warzecha CC., Guo Y., Shang X., Xing Y., Carstens RP},
    journal = {Molecular and Cellular Biology},
    title = {Determination of a comprehensive alternative splicing regulatory network and the combinatorial regulation by key factors during the epithelial to mesenchymal transition},
    number = {11},
    volume = {36},
    pages = {1704-1719},
    }

  • M Hu, L Shen, X Zan, X Shang, W Liu, "An efficient algorithm to identify the optimal one-bit perturbation based on the basin-of-state size of Boolean networks", Scientific Reports, 2016, PMC4872544.
  • [Bibtex]

    @article{nwpu_index50,
    year = {2016},
    author = {M Hu, L Shen, X Zan, X Shang, W Liu},
    journal = {Scientific Reports},
    title = {An efficient algorithm to identify the optimal one-bit perturbation based on the basin-of-state size of Boolean networks},
    pages = {PMC4872544},
    }

  • Jiang, Tao and Zhanhuai, L. I. and Shang, Xuequn and Chen, Bolin and Weibang, L. I. and Yin, Zhilei, "Constrained query of order-preserving submatrix in gene expression data", Frontiers of Computer Science, 2016, 10(6), 1-15.
  • [Bibtex]

    @article{nwpu_index51,
    year = {2016},
    author = {Jiang, Tao and Zhanhuai, L. I. and Shang, Xuequn and Chen, Bolin and Weibang, L. I. and Yin, Zhilei},
    journal = {Frontiers of Computer Science},
    title = {Constrained query of order-preserving submatrix in gene expression data},
    number = {6},
    volume = {10},
    pages = {1-15},
    }

  • B Yang, M Xiang, Y Zhang, "Multi-manifold discriminant Isomap for visualization and classification", Pattern Recognition, 2016, 55, 215-230.
  • [Bibtex]

    @article{nwpu_index64,
    year = {2016},
    author = {B Yang, M Xiang, Y Zhang},
    journal = {Pattern Recognition},
    title = {Multi-manifold discriminant Isomap for visualization and classification},
    volume = {55},
    pages = {215-230},
    }

  • Y Zhang, M Xiang, B Yang, "Linear dimensionality reduction based on Hybrid structure preserving projections", Neurocomputing, 2016, 173, 518-529.
  • [Bibtex]

    @article{nwpu_index65,
    year = {2016},
    author = {Y Zhang, M Xiang, B Yang},
    journal = {Neurocomputing},
    title = {Linear dimensionality reduction based on Hybrid structure preserving projections},
    volume = {173},
    pages = {518-529},
    }

2015

  • Jialu Hu and Knut Reinert, "LocalAli: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks", Bioinformatics, 2015, 31(3), 363-372.
  • [Bibtex]

    @article{nwpu_index14,
    year = {2015},
    author = {Jialu Hu and Knut Reinert},
    journal = {Bioinformatics},
    title = {LocalAli: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks},
    number = {3},
    volume = {31},
    pages = {363-372},
    }

  • Bolin Chen, Min Li, Jianxin Wang, Xuequn Shang, Fang-Xiang Wu, "A fast and high performance multiple data integration algorithm for identifying human disease genes", BMC Medical Genomics, 2015, 8(Suppl 3), S2.
  • [Bibtex]

    @article{nwpu_index18,
    year = {2015},
    author = {Bolin Chen, Min Li, Jianxin Wang, Xuequn Shang, Fang-Xiang Wu},
    journal = {BMC Medical Genomics},
    title = {A fast and high performance multiple data integration algorithm for identifying human disease genes},
    number = {Suppl 3},
    volume = {8},
    pages = {S2},
    abstract = { Background Integrating multiple data sources is indispensable in improving disease gene identification. It is not only due to the fact that disease genes associated with similar genetic diseases tend to lie close with each other in various biological networks, but also due to the fact that gene-disease associations are complex. Although various algorithms have been proposed to identify disease genes, their prediction performances and the computational time still should be further improved. Results In this study, we propose a fast and high performance multiple data integration algorithm for identifying human disease genes. A posterior probability of each candidate gene associated with individual diseases is calculated by using a Bayesian analysis method and a binary logistic regression model. Two prior probability estimation strategies and two feature vector construction methods are developed to test the performance of the proposed algorithm. Conclusions The proposed algorithm is not only generated predictions with high AUC scores, but also runs very fast. When only a single PPI network is employed, the AUC score is 0.769 by using F2 as feature vectors. The average running time for each leave-one-out experiment is only around 1.5 seconds. When three biological networks are integrated, the AUC score using F3 as feature vectors increases to 0.830, and the average running time for each leave-one-out experiment takes only about 12.54 seconds. It is better than many existing algorithms.},
    source = {www.biomedcentral.com/1755-8794/8/S3/S2},
    }

  • Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu, "A two-step logistic regression based algorithm for identifying individual-cancer-related genes", In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on,2015.
  • [Bibtex]

    @conference{nwpu_index23,
    year = {2015},
    author = {Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu},
    booktitle = {Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on},
    title = {A two-step logistic regression based algorithm for identifying individual-cancer-related genes},
    }

  • Jiajie Peng, Sahra Uygun, Taehyong Kim,Yadong Wang, Seung Y. Rhee, Jin Chen, "Measuring semantic similarities by combining gene ontology annotations and gene co-function networks", BMC bioinformatics, 2015, 16(1), 44.
  • [Bibtex]

    @article{nwpu_index34,
    year = {2015},
    author = {Jiajie Peng, Sahra Uygun, Taehyong Kim,Yadong Wang, Seung Y. Rhee, Jin Chen},
    journal = {BMC bioinformatics},
    title = {Measuring semantic similarities by combining gene ontology annotations and gene co-function networks},
    number = {1},
    volume = {16},
    pages = {44},
    }

  • Jialu Hu, Khiem Lam, Xiaoxi Dong, Heidi Lyng, Natalia Shulzhenko, and Andrey Morgun, "Identification of bacterial pathogens from host-microbe interaction networks", In CGRB fall conference, Corvallis, Sep 18 2015.
  • [Bibtex]

    @conference{nwpu_index40,
    year = {2015},
    author = {Jialu Hu, Khiem Lam, Xiaoxi Dong, Heidi Lyng, Natalia Shulzhenko, and Andrey Morgun},
    booktitle = {CGRB fall conference},
    title = {Identification of bacterial pathogens from host-microbe interaction networks},
    month = {Sep 18},
    address = {Corvallis},
    }

  • Khiem Lam, Jialu Hu, Xiaoxi Dong, Heidi Lyng, Natalia Shulzhenko, and Andrey Morgun, "The Microbiome of Cervical Cancer, Microbiome of cervical cancer", In Symposium on Host-Microbe Systems Biology: Synthesis and Selection of Host-Microbe Systems, Eugen, July 31-August 2 2015.
  • [Bibtex]

    @conference{nwpu_index41,
    year = {2015},
    author = {Khiem Lam, Jialu Hu, Xiaoxi Dong, Heidi Lyng, Natalia Shulzhenko, and Andrey Morgun},
    booktitle = {Symposium on Host-Microbe Systems Biology: Synthesis and Selection of Host-Microbe Systems},
    title = {The Microbiome of Cervical Cancer, Microbiome of cervical cancer},
    month = {July 31-August 2},
    address = {Eugen},
    }

  • Xiaoxi Dong, Jialu Hu, Ekaterian Peremyslova, Ivan J. Fuss, Michael Yao, Warren Strober, Natalia Shulzhenko, Andrey Morgun, "Shotgun sequencing reveals transkingdom alterations inimmunodeficiency associated enteropathy", In Symposium on Host-Microbe Systems Biology: Synthesis and Selection of Host-Microbe Systems, Eugen, July 31-August 2 2015.
  • [Bibtex]

    @conference{nwpu_index42,
    year = {2015},
    author = {Xiaoxi Dong, Jialu Hu, Ekaterian Peremyslova, Ivan J. Fuss, Michael Yao, Warren Strober, Natalia Shulzhenko, Andrey Morgun},
    booktitle = {Symposium on Host-Microbe Systems Biology: Synthesis and Selection of Host-Microbe Systems},
    title = {Shotgun sequencing reveals transkingdom alterations inimmunodeficiency associated enteropathy},
    month = {July 31-August 2},
    address = {Eugen},
    }

  • Li, X. and Shen, L. and Shang, X. and Liu, W., "Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway", Plos One, 2015, 10(7), e0132813.
  • [Bibtex]

    @article{nwpu_index52,
    year = {2015},
    author = {Li, X. and Shen, L. and Shang, X. and Liu, W.},
    journal = {Plos One},
    title = {Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway},
    number = {7},
    volume = {10},
    pages = {e0132813},
    }

  • Qiben Zheng, Liangzhong Shen, Xuequn Shang, Wenbin Liu, "Detecting small attractors of large boolean networks by function-reduction-based strategy", IET Systems Biology, 2015(27), 1-8.
  • [Bibtex]

    @article{nwpu_index53,
    year = {2015},
    author = {Qiben Zheng, Liangzhong Shen, Xuequn Shang, Wenbin Liu},
    journal = {IET Systems Biology},
    title = {Detecting small attractors of large boolean networks by function-reduction-based strategy},
    number = {27},
    pages = {1-8},
    }

  • B Yang, M Xiang, Y Zhang, "Learning discriminant Isomap for dimensionality reduction", In Neural Networks (IJCNN), 1-8,2015.
  • [Bibtex]

    @conference{nwpu_index69,
    year = {2015},
    author = {B Yang, M Xiang, Y Zhang},
    booktitle = {Neural Networks (IJCNN)},
    title = {Learning discriminant Isomap for dimensionality reduction},
    pages = {1-8},
    }

2014

  • Bolin Chen, Weiwei Fan, Juan Liu and Fangxiang Wu, " Identifying protein complexes and functional modules-from static PPI networks to dynamic PPI networks ", Briefings in Bioinformatics, 2014, 15(2), 177-194.
  • [Bibtex]

    @article{nwpu_index8,
    year = {2014},
    author = {Bolin Chen, Weiwei Fan, Juan Liu and Fangxiang Wu},
    journal = {Briefings in Bioinformatics},
    title = { Identifying protein complexes and functional modules-from static PPI networks to dynamic PPI networks },
    number = {2},
    volume = {15},
    pages = {177-194},
    abstract = {Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein–protein interaction (PPI) networks. We first describe issues and pitfalls when interpreting PPI networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted PPI networks; (ii) those based on characters of weighted PPI networks; (iii) those based on multiple data integrations; and (iv) those based on dynamic PPI networks. The PPI networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic P},
    source = {http://bib.oxfordjournals.org/content/15/2/177.long},
    }

  • Jialu Hu, Birte Kehr and Knut Reinert, "NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks", Bioinformatics, 2014, 30(4), 540.
  • [Bibtex]

    @article{nwpu_index15,
    year = {2014},
    author = {Jialu Hu, Birte Kehr and Knut Reinert},
    journal = {Bioinformatics},
    title = {NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks},
    number = {4},
    volume = {30},
    pages = {540},
    }

  • Bolin Chen, Min Li, Jianxin Wang, Fang-Xiang Wu, "Disease gene identification by using graph kernels and Markov random fields", SCIENCE CHINA Life Science, 2014, 57(11), 1054-1063.
  • [Bibtex]

    @article{nwpu_index19,
    year = {2014},
    author = {Bolin Chen, Min Li, Jianxin Wang, Fang-Xiang Wu},
    journal = {SCIENCE CHINA Life Science},
    title = {Disease gene identification by using graph kernels and Markov random fields},
    number = {11},
    volume = {57},
    pages = {1054-1063},
    abstract = {Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.},
    source = {http://link.springer.com/article/10.1007%2Fs11427-014-4745-8},
    }

  • Bolin Chen, Jianxin Wang, Min Li, Fang-Xiang Wu, "Identifying disease genes by integrating multiple data sources", BMC Medical Genomics, 2014, 7(Suppl 2), S2.
  • [Bibtex]

    @article{nwpu_index20,
    year = {2014},
    author = {Bolin Chen, Jianxin Wang, Min Li, Fang-Xiang Wu},
    journal = {BMC Medical Genomics},
    title = {Identifying disease genes by integrating multiple data sources},
    number = {Suppl 2},
    volume = {7},
    pages = {S2},
    abstract = { Background Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes. Results In this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes. Conclusions Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.},
    source = {http://bmcmedgenomics.biomedcentral.com/articles/10.1186/1755-8794-7-S2-S2},
    }

  • Jiajie Peng, Hongxiang Li, Qinghua Jiang, Yadong Wang and Jin Chen, "An Integrative Approach for Measuring Semantic Similarities using Gene Ontology ", BMC systems biology, 2014, 8(5), S8.
  • [Bibtex]

    @article{nwpu_index31,
    year = {2014},
    author = {Jiajie Peng, Hongxiang Li, Qinghua Jiang, Yadong Wang and Jin Chen},
    journal = {BMC systems biology},
    title = {An Integrative Approach for Measuring Semantic Similarities using Gene Ontology },
    number = {5},
    volume = {8},
    pages = {S8},
    }

2013

  • Bolin Chen, Fang-Xiang Wu, "Identifying protein complexes based on multiple topological structures in PPI networks", IEEE Transactions on Nanobioscience, 2013, 12(3), 165-172.
  • [Bibtex]

    @article{nwpu_index21,
    year = {2013},
    author = {Bolin Chen, Fang-Xiang Wu},
    journal = {IEEE Transactions on Nanobioscience},
    title = {Identifying protein complexes based on multiple topological structures in PPI networks},
    number = {3},
    volume = {12},
    pages = {165-172},
    abstract = {Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO.},
    source = {http://ieeexplore.ieee.org/document/6583247/?reload=true&arnumber=6583247},
    }

  • Bolin Chen, Jinhong Shi, Shenggui Zhang, Fang-Xiang Wu, "Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy", Proteomics, 2013, 13(2), 269-277.
  • [Bibtex]

    @article{nwpu_index22,
    year = {2013},
    author = {Bolin Chen, Jinhong Shi, Shenggui Zhang, Fang-Xiang Wu},
    journal = {Proteomics},
    title = {Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy},
    number = {2},
    volume = {13},
    pages = {269-277},
    abstract = {The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one.},
    source = {http://onlinelibrary.wiley.com/doi/10.1002/pmic.201200336/abstract},
    }

  • Jiajie Peng, Jin Chen, Yadong Wang, "Identifying cross-category relations in gene ontology and constructing genome-specific term association networks ", BMC bioinformatics, 2013, 14(2), S15.
  • [Bibtex]

    @article{nwpu_index32,
    year = {2013},
    author = {Jiajie Peng, Jin Chen, Yadong Wang},
    journal = {BMC bioinformatics},
    title = {Identifying cross-category relations in gene ontology and constructing genome-specific term association networks },
    number = {2},
    volume = {14},
    pages = {S15},
    }

  • Jialu Hu, Birte Kehr, and Knut Reinert, M-NetAligner, "a novel global alignment approach to identify functional orthologs in multiple networks", In 17th Annual International Conference on Research in Computational Molecular Biology (RECOMB), P207, Beijing, Apr 7-10 2013.
  • [Bibtex]

    @conference{nwpu_index39,
    year = {2013},
    author = {Jialu Hu, Birte Kehr, and Knut Reinert, M-NetAligner},
    booktitle = {17th Annual International Conference on Research in Computational Molecular Biology (RECOMB)},
    title = {a novel global alignment approach to identify functional orthologs in multiple networks},
    month = {Apr 7-10},
    pages = {P207},
    address = {Beijing},
    }

2011

  • Jialu Hu, Ling Sun, Liang Yu, Lin Gao, "A Novel Graph Isomorphism Algorithm Based on Feature Selection in Network Motif Discovery", Sciencepaper online, 2011.
  • [Bibtex]

    @article{nwpu_index37,
    year = {2011},
    author = {Jialu Hu, Ling Sun, Liang Yu, Lin Gao},
    journal = {Sciencepaper online},
    title = {A Novel Graph Isomorphism Algorithm Based on Feature Selection in Network Motif Discovery},
    }

2009

  • Qin Gui-min, Hu Jia-lu, Gao Lin, "A review on algorithms for network motif discovery in biological networks", Chinese Journal of Electronics, 2009, 37(10), 2258-2265.
  • [Bibtex]

    @article{nwpu_index36,
    year = {2009},
    author = {Qin Gui-min, Hu Jia-lu, Gao Lin},
    journal = {Chinese Journal of Electronics},
    title = {A review on algorithms for network motif discovery in biological networks},
    number = {10},
    volume = {37},
    pages = {2258-2265},
    }

  • Jialu Hu, Lin Gao, and Guimin Qin, "Evaluation of subgraph searching algorithms detecting network motif in biological networks", In Frontiers of Computer Science, 412-416, China,2009.
  • [Bibtex]

    @conference{nwpu_index38,
    year = {2009},
    author = {Jialu Hu, Lin Gao, and Guimin Qin},
    booktitle = {Frontiers of Computer Science},
    title = {Evaluation of subgraph searching algorithms detecting network motif in biological networks},
    pages = {412-416},
    address = {China},
    }